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ORCID29th ESANN 2021: Online event (Bruges, Belgium)
- 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021, Online event (Bruges, Belgium), October 6-8, 2021. 2021
Federated Learning - Methods, Applications and Beyond
- Moritz Heusinger, Christoph Raab, Fabrice Rossi, Frank-Michael Schleif
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Federated Learning - Methods, Applications and beyond. - Mirko Polato, Alberto Gallinaro, Fabio Aiolli:
Privacy-Preserving Kernel Computation For Vertically Partitioned Data. - Miguel Fernandes, Catarina Silva, Joel Arrais, Alberto Cardoso, Bernardete Ribeiro:
Decay Momentum for Improving Federated Learning. - Lorenzo Pellegrini, Vincenzo Lomonaco, Gabriele Graffieti, Davide Maltoni:
Continual Learning at the Edge: Real-Time Training on Smartphone Devices. - Johannes Brinkrolf, Barbara Hammer:
Federated Learning Vector Quantization.
Evaluation metrics, and concept drift
- Adrien Pavao, Isabelle Guyon, Michael Vaccaro:
Judging competitions and benchmarks: a candidate election approach. - Fabian Hinder, Barbara Hammer:
Concept Drift Segmentation via Kolmogorov-Trees. - Valerie Vaquet, Patrick Menz, Udo Seiffert, Barbara Hammer:
Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data. - Abdel-Rahmen Korichi, Hamamache Kheddouci, Daniel West:
Predicting employee attrition with a more effective use of historical events. - Aashi Jindal, Prashant Gupta, Debarka Sengupta, Jayadeva:
Enhash: A Fast Streaming Algorithm For Concept Drift Detection. - Vadym Gryshchuk, Cornelius Weber, Chu Kiong Loo, Stefan Wermter:
Lifelong Learning from Event-based Data. - Sebastian Hoch, Sascha Lange, Janis Keuper:
Sample efficient localization and stage prediction with autoencoders. - Valentin Hamaide, François Glineur:
Transfer learning in Bayesian optimization for the calibration of a beam line in proton therapy. - Christoph Raab, Sascha Saralajew, Frank-Michael Schleif:
Domain Adversarial Tangent Learning Towards Interpretable Domain Adaptation.
Deep learning for graphs
- Davide Bacciu, Filippo Maria Bianchi, Benjamin Paassen, Cesare Alippi:
Deep learning for graphs. - Domenico Tortorella, Alessio Micheli:
Dynamic Graph Echo State Networks. - Erik Jhones F. do Nascimento, Amauri H. Souza, Diego Mesquita:
Improving Graph Variational Autoencoders with Multi-Hop Simple Convolutions. - Luca Hermes, Barbara Hammer, Malte Schilling:
Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting. - Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Tangent Graph Convolutional Network. - Tim Cofala, Oliver Kramer:
Transformers for Molecular Graph Generation. - Marco Trincavelli, Haris Dukic, Georgios Deligiorgis, Pierpaolo Sepe, Davide Bacciu:
Inductive learning for product assortment graph completion.
Deep learning and image processing
- Patrick Burke, Jonas Prellberg, Oliver Kramer:
Evolutionary Deep Multi-Task Learning. - Hafez Farazi, Jan Nogga, Sven Behnke:
Semantic Prediction: Which One Should Come First, Recognition or Prediction? - Tomasz Stanczyk, Siamak Mehrkanoon:
Deep Graph Convolutional Networks for Wind Speed Prediction. - Stéphane Chrétien, Emmanuel Caron:
Benign overfitting of fully connected Deep Nets: A Sobolev space viewpoint. - Slawomir Golak:
Correlated Weights Neural Layer with external control. - Daniel I. Morís, Joaquim de Moura, Jorge Novo, Marcos Ortega:
Comprehensive Analysis of the Screening of COVID-19 Approaches in Chest X-ray Images from Portable Devices. - Shruti Kunde, Amey Pandit, Kushagra Mahajan, Monika Sharma, Rekha Singhal, Lovekesh Vig:
Data-Efficient Training of High-Resolution Images in Medical Domain. - Rawaa Hamdi, Asma Kerkeni, Mohamed Hédi Bedoui, Asma Ben Abdallah:
CAS-Net: A Novel Coronary Artery Segmentation Neural Network. - Ismail Alaoui Abdellaoui, Jesús García Fernández, Caner Sahinli, Siamak Mehrkanoon:
Enhancing brain decoding using attention augmented deep neural networks. - Jérôme Treboux, Rolf Ingold, Dominique Genoud:
Improved and Generalized Vine Line Detection on Aerial Images Using Asymmetrical Neural Networks and ML Subclassifiers. - Haodi Zhang, Alexandrina Rogozan, Abdelaziz Bensrhair:
Cross-modal verification for 3D object detection. - Malte Mosbach, Sven Behnke:
Fourier-based Video Prediction through Relational Object Motion. - Maxence Chaverot, Maxime Carré, Michel Jourlin, Abdelaziz Bensrhair, Richard Grisel:
Object Detection on Thermal Images: Performance of YOLOv4 Trained on Small Datasets. - Chanho Kim, Won-Sook Lee:
Temperature as a Regularizer for Semantic Segmentation.
Machine Learning for Measuring and Analyzing Online Social Communications
- Chris Bronk, Amaury Lendasse, Peggy Lindner, Dan S. Wallach, Barbara Hammer:
Machine Learning for Measuring and Analyzing Online Social Communications. - Max Lübbering, Maren Pielka, Kajaree Das, Michael Gebauer, Rajkumar Ramamurthy, Christian Bauckhage, Rafet Sifa:
Toxicity Detection in Online Comments with Limited Data: A Comparative Analysis. - Megumi Kawase:
Emotional Intensity Level Analysis of Speech Emotional Intensity Estimation.
Natural language processing
- Antonio Sorgente, Massimo De Gregorio, Giuseppe Vettigli:
Weightless Neural Networks for text classification using tf-idf. - Iván Vallés-Pérez, Juan Gómez-Sanchís, Marcelino Martínez-Sober, Joan Vila-Francés, Antonio José Serrano-López, Emilio Soria-Olivas:
End-to-end Keyword Spotting using Xception-1d. - Thomas Luka, Laure Soulier, David Picard:
Unsupervised Word Representations Learning with Bilinear Convolutional Network on Characters. - Arushi Jain, Shubham Paliwal, Monika Sharma, Lovekesh Vig:
TSR-DSAW: Table Structure Recognition via Deep Spatial Association of Words. - Florian Barbaro, Fabrice Rossi:
Sparse mixture of von Mises-Fisher distribution. - Gil Rocha, Henrique Lopes Cardoso:
Towards Robust Auxiliary Tasks for Language Adaptation.
Recurrent learning, and reinforcement learning
- Andrea Cossu, Davide Bacciu, Antonio Carta, Claudio Gallicchio, Vincenzo Lomonaco:
Continual Learning with Echo State Networks. - Jensun Ravichandran, Thomas Villmann, Marika Kaden:
RecLVQ: Recurrent Learning Vector Quantization. - Beatriz P. Santos, Maryam Abbasi, Tiago Pereira, Bernardete Ribeiro, Joel Arrais:
Improvement on Generative Adversarial Network for Targeted Drug Design. - Claudio Gallicchio:
Reservoir Computing by Discretizing ODEs. - Jochen J. Steil, Yannic Lieder:
Constraint optimization for Echo State Networks applied to satellite image forecasting. - Luca Pedrelli, Marco Tramontano, Giuseppe Vannozzi, Andrea Mannini:
Deep Echo State Networks for Functional Ambulation Categories Estimation. - Martin Böhm, Thomas Schmid:
An Algorithmic Approach to Establish a Lower Bound for the Size of Semiring Neural Networks. - Ismael Matino, Stefano Dettori, Valentina Colla, Katharina Rechberger, Nina Kieberger:
Echo-state neural networks forecasting steelworks off-gases for their dispatching in CH4 and CH3OH syntheses reactors. - Raphaël Langhendries, Jérôme Lacaille:
Deep Learning Model for Context-Dependent Survival Analysis. - Phillip Swazinna, Steffen Udluft, Daniel Hein, Thomas A. Runkler:
Behavior Constraining in Weight Space for Offline Reinforcement Learning. - Maryam Abbasi, Tiago Pereira, Beatriz P. Santos, Bernardete Ribeiro, Joel Arrais:
Multiobjective Reinforcement Learning in Optimized Drug Design. - Antonin Calba, Alain Dutech, Jérémy Fix:
Density Independent Self-organized Support for Q-Value Function Interpolation in Reinforcement Learning.
Complex Data: Learning Trustworthily, Automatically, and with Guarantees
- Luca Oneto, Nicolò Navarin, Battista Biggio, Federico Errica, Alessio Micheli, Franco Scarselli, Monica Bianchini, Alessandro Sperduti:
Complex Data: Learning Trustworthily, Automatically, and with Guarantees. - Luca Oneto, Sandro Ridella, Davide Anguita:
The Benefits of Adversarial Defence in Generalisation. - Maura Pintor, Luca Demetrio, Giovanni Manca, Battista Biggio, Fabio Roli:
Slope: A First-order Approach for Measuring Gradient Obfuscation. - Federico Errica, Giacomo Iadarola, Fabio Martinelli, Francesco Mercaldo, Alessio Micheli:
Robust Malware Classification via Deep Graph Networks on Call Graph Topologies. - Géraldin Nanfack, Valentin Delchevalerie, Benoît Frénay:
Boundary-Based Fairness Constraints in Decision Trees and Random Forests.
Model selection
- Amaury Lendasse, Kallin Khan, Edward R. Ratner:
NNBMSS: a Novel and Fast Method for Model Structure Selection. - Vincent Rolfs, Matthias Kerzel, Stefan Wermter:
Pruning Neural Networks with Supermasks. - René Traoré, Andrés Camero, Xiaoxiang Zhu:
Compact Neural Architecture Search for Local Climate Zones Classification.
Unsupervised learning
- Corentin Larroche, Johan Mazel, Stéphan Clémençon:
Anomalous Cluster Detection in Large Networks with Diffusion-Percolation Testing. - Andrea Valenti, Stefano Berti, Davide Bacciu:
Calliope - A Polyphonic Music Transformer. - Louise Bonfils, Allou Samé, Latifa Oukhellou:
Dynamic clustering and modeling of temporal data subject to common regressive effects. - Pierre Lambert, Cyril de Bodt, Michel Verleysen, John A. Lee:
Stochastic quartet approach for fast multidimensional scaling. - Elena Hernández-Pereira, Oscar Fontenla-Romero, Bertha Guijarro-Berdiñas, Beatriz Pérez-Sánchez:
Federated Learning approach for SpectralClustering. - Attila Mester, Zalán Bodó:
Validating static call graph-based malware signatures using community detection methods. - Pierre Lambert, John A. Lee, Michel Verleysen, Cyril de Bodt:
Impact of data subsamplings in Fast Multi-Scale Neighbor Embedding. - Naresh Balaji Ravichandran, Anders Lansner, Pawel Andrzej Herman:
Semi-supervised learning with Bayesian Confidence Propagation Neural Network. - Valentina Poggioni, Alina Elena Baia, Alfredo Milani:
Combining Attack Success Rate and DetectionRate for effective Universal Adversarial Attacks.
Machine learning and data mining for urban mobility intelligence
- Etienne Côme, Latifa Oukhellou, Allou Samé, Lijun Sun:
Machine learning and data mining for urban mobility intelligence. - Étienne Goffinet, Mustapha Lebbah, Hanane Azzag, Loïc Giraldi, Anthony Coutant:
Multivariate Time Series Multi-Coclustering. Application to Advanced Driving Assistance System Validation. - Evelyne Akopyan, Angelo Furno, Nour-Eddin El Faouzi, Eric Gaume:
Unsupervised Real-time Anomaly Detection for Multivariate Mobile Phone Traffic Series. - Gianluca Boleto, Luca Oneto, Matteo Cardellini, Marco Maratea, Mauro Vallati, Renzo Canepa, Davide Anguita:
In-Station Train Movements Prediction: from Shallow to Deep Multi Scale Models. - Milad Leyli-Abadi, Abderrahmane Boubezoul:
Deep Neural Networks for Classification of Riding Patterns: with a focus on explainability. - Benoit Matet, Etienne Côme, Angelo Furno, Loïc Bonnetain, Latifa Oukhellou, Nour-Eddin El Faouzi:
A Lightweight Approach for Origin-Destination Matrix Anonymization.
Supervised learning
- Laurine Duchesne, Quentin Louveaux, Louis Wehenkel:
Supervised learning of convex piecewise linear approximations of optimization problems. - Lode Vuegen, Peter Karsmakers:
Real-time On-edge Classification: an Application to Domestic Acoustic Event Recognition. - Rafael Fontella Katopodis, Priscila Lima, Felipe M. G. França:
Functional Gradient Descent for n-Tuple Regression. - John A. Lee, Alyssa Vanginderdeuren, Margerie Huet-Dastarac, Ana Maria Barragán-Montero:
Estimating uncertainty in radiation oncology dose prediction with dropout and bootstrap in U-Net models. - Matthias Brucklacher, Hanspeter A. Mallot, Tristan Baumann:
Hierarchical Planning in Multilayered State-Action Networks. - Tobias Leemann, Moritz Sackmann, Jörn Thielecke, Ulrich Hofmann:
Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling. - Antti Pihlajamäki, Joakim Linja, Joonas Hämäläinen, Paavo Nieminen, Sami Malola, Tommi Kärkkäinen, Hannu Häkkinen:
Orientation Adaptive Minimal Learning Machine for Directions of Atomic Forces. - Fech Scen Khoo, Dawei Zhu, Michael A. Hedderich, Dietrich Klakow:
Estimating Formulas for Model Performance Under Noisy Labels Using Symbolic Regression. - Baichuan Chi, Amaury Lendasse, Edward R. Ratner, Renjie Hu:
A Multi-ELM Model for Incomplete Data.
Interpretable Models in Machine Learning and Explainable Artificial Intelligence
- Paulo Lisboa, Sascha Saralajew, Alfredo Vellido, Thomas Villmann:
The Coming of Age of Interpretable and Explainable Machine Learning Models. - Torben Gräber, Sebastian Vetter, Sascha Sarajalew, Michael Unterreiner, Dieter Schramm:
AGLVQ - Making Generalized Vector Quantization Algorithms Aware of Context. - Joshua Taylor, Erzsébet Merényi:
A Parameterless t-SNE for Faithful Cluster Embeddings from Prototype-based Learning and CONN Similarity. - Marie Chavent, Jérôme Lacaille, Alex Mourer, Madalina Olteanu:
Handling Correlations in Random Forests: which Impacts on Variable Importance and Model Interpretability? - Bradley Walters, Sandra Ortega-Martorell, Iván Olier, Paulo Lisboa:
The partial response SVM. - Marika Kaden, Ronny Schubert, Mehrdad Mohannazadeh Bakhtiari, Lucas Schwarz, Thomas Villmann:
The LVQ-based Counter Propagation Network - an Interpretable Information Bottleneck Approach. - Madina Babazhanova, Maxat Tezekbayev, Zhenisbek Assylbekov:
Geometric Probing of Word Vectors. - Manik Madhikermi, Avleen Malhi, Kary Främling:
Context-specific sampling method for contextual explanations. - Arne P. Raulf, Sina Däubener, Ben Hack, Axel Mosig, Asja Fischer:
SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations. - Cosimo Izzo, Aldo Lipani, Ramin Okhrati, Francesca Medda:
A Baseline for Shapley Values in MLPs: from Missingness to Neutrality.
Time series and signal processing
- Karan Bhanot, Saloni Dash, Joseph Pedersen, Isabelle Guyon, Kristin P. Bennett:
Quantifying Resemblance of Synthetic Medical Time-Series. - Hiba Arnout, Johanna Bronner, Thomas A. Runkler:
Differentially Private Time Series Generation. - Rémi Souriau, Julie Fontecave Jallon, Bertrand Rivet:
Fusion of estimations from two modalities using the Viterbi's algorithm: application to fetal heart rate monitoring. - Delphine Bay, Clémence Bisot:
Convolutional Neural Network Architecture for Classification of Aircraft Engines Flight Time Series. - Maximilian Münch, Simon Heilig, Frank-Michael Schleif:
Multi-perspective embedding for non-metric time series classification. - Sarthak Chatterjee, Subhro Das, Sérgio Pequito:
IF: Iterative Fractional Optimization.
Classification
- Arturs Polis, Alexander Ilin:
A Relational Model for One-Shot Classification. - Joonas Hämäläinen, Paavo Nieminen, Tommi Kärkkäinen:
Instance-Based Multi-Label Classification via Multi-Target Distance Regression. - Raul Barbosa, Diego Carvalho, Priscila Lima, Felipe M. G. França:
A bag of nodes primer on weightless graph classification. - Bálint Daróczy, Dániel Rácz:
Gradient representations in ReLU networks as similarity functions.
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